embeddinggemma-300m-bf16

Name: embeddinggemma-300m-bf16
Author: mlx-community

384

1 language

—

mlx-community

Embedding Model

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New

384 downloads

Early-stage

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Quick Summary

The Model mlx-community/embeddinggemma-300m-bf16 was converted to MLX format from google/embeddinggemma-300m using mlx-lm version 0.

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by embeddinggemma-300m-bf16 with quality assessment

Specialized For

general

science

multilingual

reasoning

Training Datasets (3)

common crawl

🔴 2.5/10

general

science

Key Strengths

•Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
•Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
•Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...

Considerations

•Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
•Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...

wikipedia

🟡 5/10

science

multilingual

Key Strengths

•High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
•Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
•Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...

Considerations

•Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
•Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...

arxiv

🟡 5.5/10

science

reasoning

Key Strengths

•Scientific Authority: Peer-reviewed content from established repository
•Domain-Specific: Specialized vocabulary and concepts
•Mathematical Content: Includes complex equations and notation

Considerations

•Specialized: Primarily technical and mathematical content
•English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxbash

pip install mlx-embeddings

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

Use with mlxpython

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16")


# For text embedding
sentences = [
    "task: sentence similarity | query: Nothing really matters.",
    "task: sentence similarity | query: The dog is barking.",
    "task: sentence similarity | query: The dog is barking.",
]

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')

# Compute token embeddings
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
output = model(input_ids, attention_mask)

embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)


# You can use these task-specific prefixes for different tasks
task_prefixes = {
    "BitextMining": "task: search result | query: ",
    "Clustering": "task: clustering | query: ",
    "Classification": "task: classification | query: ",
    "MultilabelClassification": "task: classification | query: ",
    "PairClassification": "task: sentence similarity | query: ",
    "InstructionRetrieval": "task: code retrieval | query: ",
    "Reranking": "task: search result | query: ",
    "Retrieval": "task: search result | query: ",
    "Retrieval-query": "task: search result | query: ",
    "Retrieval-document": "title: none | text: ",
    "STS": "task: sentence similarity | query: ",
    "Summarization": "task: summarization | query: ",
    "document": "title: none | text: "
}

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